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. 2019 Jan 22;11(2):501–522. doi: 10.18632/aging.101756

Prognostic value of immune checkpoint molecules in head and neck cancer: a meta-analysis

Yi-Qun Jia 1,*, Bo Yang 1,*, Li-Ling Wen 1, Wen-Xin Mu 1, Zhi Wang 1, Bin Cheng 1,✉
PMCID: PMC6366990  PMID: 30668545

Abstract

Immune checkpoint molecules are important targets in cancer immunotherapy, but their association with prognosis in patients with head and neck cancer is controversial. In this meta-analysis, we searched for 12 immune checkpoint molecules in the PubMed, Embase and Cochrane Library databases and retrieved 52 studies with 7127 participants. Among the molecules included in the search, indoleamine 2, 3-dioxygenase (IDO), programmed death ligand 1 (PD-L1), and programmed death 1 (PD-1) met the inclusion criteria for further analysis. Higher expression of IDO was associated with poorer overall survival in head and neck cancer patients (P = 0.011), but higher expression of PD-L1 correlated with better overall survival specifically in nasopharyngeal carcinoma patients (P = 0.01). In a sensitivity analysis, higher PD-L1 expression correlated with better progression-free survival (P = 0.043), and was associated with better overall survival in Caucasian subjects (P = 0.02), nasopharyngeal carcinoma patients (P = 0.015), and studies with small sample sizes (P = 0.001). PD-1 had no prognostic significance. There was no publication bias affecting the results. Thus, among the immune checkpoint molecules, IDO and PD-L1 are potential prognostic predictors in head and neck cancer.

Keywords: immune checkpoint molecule, prognosis, survival, head and neck cancer, meta-analysis

Introduction

Head and neck cancer (HNC) is the sixth most common malignancy worldwide [1]. Most patients exhibit advanced-stage disease, including regional lymph node involvement, and 10% of patients have distant metastases [2]. The traditional treatment options for HNC are surgery, radiotherapy and chemotherapy [3], which have severe adverse effects. Furthermore, some patients do not benefit much from these treatments, and are likely to relapse. Anatomic complexities often lead to malfunctions in speaking, swallowing and breathing after treatments, hampering patients’ long-term quality of life [4]. Although there have been certain advances in treatment, the overall survival of HNC patients is still unsatisfactory, and the five-year survival rate is less than 50% [5–7].

Immunosuppressive patients are prone to suffer from HNC [8], although the predominant causes of HNC are tobacco and alcohol consumption [4] and viral infections [9,10]. Among the functions of the immune cells, immune checkpoint activity has been reported to be involved in the surveillance of tumor development and progression [11]. Immune checkpoint molecules including programmed death 1 (PD-1) [12,13], indoleamine 2, 3-dioxygenase (IDO) [14,15], B7-H3 [16,17], lymphocyte activation gene 3 (LAG-3) [18], cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) [19], programmed death ligand 2 (PD-L2) [20], V-domain Ig suppressor of T cell activation (VISTA) [21], B7-H4 [22] and programmed death ligand 1 (PD-L1) [23–25] have been used as markers to evaluate the prognosis of HNC. However, the survival rates of patients with high expression of immune checkpoint molecules have differed according to the overexpressed molecule.

In the present study, we performed a systematic review of the available literature on this topic in PubMed, Embase and the Cochrane Library. Then, we conducted a meta-analysis of the survival rates (including overall survival [OS], disease-free survival [DFS], progression-free survival [PFS], disease-specific survival [DSS] and distant metastases-free survival [DMFS]) of patients expressing different levels of immune checkpoint molecules.

RESULTS

Study characteristics

The characteristics of the included studies are shown in Table 1. There were 52 prospective studies comparing contemporary series of patients (level of evidence: 3b) in 51 articles. These studies included 7127 patients and met the criteria for meta-analysis. The literature selection procedure is presented in Figure 1. The included articles were evaluated by the Newcastle–Ottawa Scale (NOS; Supplementary Table 1), and all the articles were published between 2010 and 2018. Roughly half of the studies were conducted in Asia (n=23), while the remainder were conducted in Europe (n=18), North and South America (n=6), Oceania (n=4) and Africa (n=1). Thus, the samples included in this meta-analysis covered most of the continents of the world. In terms of the immune checkpoint molecules, the majority of the studies evaluated PD-L1 (n=40), while the rest assessed PD-1 (n=8) and IDO (n=4). The sample sizes of the included studies ranged from 38 to 517. With reference to the mean value of all the samples, 17 studies were considered to have a large sample size (n > 139), while 35 had a small sample size (n ≤ 139). Forty-three studies explored the prognostic value of their chosen immune checkpoint molecule for OS, 19 for DFS, 6 for PFS, 5 for DSS and 3 for DMFS.

Table 1. Characteristics of included studies.

Author and year Target Country /
Region		 Ethnicity Tumor location Sample size Gender M/F Cut-off value Detection method TNM stage Outcome HR estimation Study design NOS score
Ahn et al. 2017 [23] PD-L1 Korea Asian OSCC 68 45/23 Grade > 1 IHC I-IV OS DFS reported P 7
Badoual et al. 2013 [12] PD-1 France Caucasian HNSCC 64 NA > median IF I-IV OS reported P 6
Balempas et al. 2017 [36] PD-L1 Germany Caucasian HNSCC 161 131/30 > 5% IHC I-IV OS DMFS reported P 7
Ben-Haj-Ayed et al. 2016 [14] IDO Tunisia Caucasian NPC 71 48/23 > median IHC I-IV OS DFS reported P 7
Birtalan et al. 2017 [24] PD-L1 Hungary Caucasian HNSCC 106 90/16 Score > 0% IHC I-IV DSS reported P 6
Budczies et al. 2016 [25] PD-L1 Germany Caucasian HNSCC 517 NA > median qRT-PCR NA OS DFS reported P 5
Chan et al. 2017 [46] PD-L1 USA Caucasian NPC 161 117/44 ≥ 1% IHC I-IV OS PFS reported P 6
Chang et al. 2017 [47] PD-L1 Philippines Asian NPC 56 43/13 > 1% IHC I-IV OS reported P 5
Chen et al. 2015 [48] PD-L1 Taiwan Asian OSCC 218 145/73 > 5% IHC I-IV OS reported P 7
Chen et al. 2017 [49] PD-L1 China Asian HNSCC 496 NA > 5% qRT-PCR I-IV OS reported P 7
Cho et al. 2011 [50] PD-L1 Korea Asian OSCC 45 32/13 Grade > 1 IHC I-IV OS estimated P 6
De Meulenaere et al. 2017 [51] PD-L1 Belgium Caucasian OSCC 99 82/17 > 1% IHC I-IV OS DFS reported P 6
Fang et al. 2014 [52] PD-L1 China Asian NPC 139 113/26 > 35% IHC I-IV DFS estimated P 6
Feng et al. 2017 [53] PD-L1 USA Caucasian OSCC 119 74/45 < 30 μm IHC I-IV OS estimated P 6
Fiedler et al. 2018 [54] PD-L1 Germany Caucasian HNSCC 82 73/9 > 5% IHC I-IV OS reported P 7
Hanna et al. 2018 [37] PD-L1 USA Caucasian OSCC 81 49/32 > 10% IHC I-IV OS reported P 7
Hong et al. 2016 [55] PD-L1 Australia Caucasian OSCC 99 79/20 > 25% IHC I-IV OS reported P 6
Hsu et al. 2010 [13] PD-1 Taiwan Asian NPC 46 39/7 > median IHC NA OS DFS reported P 4
Kansy et al. 2017 [56] PD-1 Germany Caucasian HNSCC 56 NA NA FACS I-IV DFS reported P 6
Kim et al. 2016 [57] PD-1 Korea Asian HNSCC 402 302/100 > 5% IHC I-IV OS DFS reported P 6
Kim et al. 2016 [58] PD-1 Korea Asian OSCC 133 120/13 > 5% IHC I-IV OS reported P 7
Kogashiwa et al. 2017 [35] PD-L1 Japan Asian OSCC 84 57/27 > 5% IHC I-IV OS PFS reported P 7
Laimer et al. 2011 [15] IDO Austria Caucasian OSCC 88 67/21 > 4 IHC I-IV OS reported P 7
Larbcharoensub et al. 2018 [59] PD-L1 Thailand Asian NPC 114 77/67 ≥ 5% IHC I-IV OS estimated P 7
Lee et al. 2016 [60] PD-L1 Hong Kong Asian NPC 104 85/19 > 1 IHC I-IV PFS DMFS OS reported P 5
Li et al. 2017 [61] PD-L1 China Asian NPC 62 40/14 > 20% IHC I-IV DFS reported P 5
Lin et al. 2015 [30] PD-L1 Taiwan Asian OSCC 305 236/69 > 1 IHC I-IV OS reported P 6
Muller et al. 2017 [62] PD-L1 Germany Caucasian HNSCC 293 82/16 142/53 (224/69) Score ≥ 1 IHC I-IV OS reported P 6
Ock et al. 2016 [63] PD-L1 South Korea Asian HNSCC 141 40/10 61/30 (101/40) ≥ 5% IHC I-IV OS reported P 6
Oguejiofor et al. 2017 [64] PD-L1 UK Caucasian OPSCC 124 NA > 5% IHC I-IV OS reported P 7
Oliveira-Costa et al. 2015 [65] PD-L1 Brazil Caucasian OSCC 142 125/17 ≥ 5% IHC I-III DSS reported P 6
Ono et al. 2017 [66] PD-L1 Japan Asian HPSCC 83 79/4 ≥ 1% IHC III-IV OS PFS reported P 6
Ono et al. 2018 [67] PD-L1 Japan Asian NPC 66 54/12 ≥ 5% IHC I-IV OS PFS reported P 7
Ou et al. 2017 [68] PD-L1 France Caucasian HNSCC 38 NA ≥ 1% IHC III-IV OS PFS estimated P 7
Qu et al. 2018 [69] PD-L1 China Asian NPC 96 72/24 > 10% IHC I-IV DMFS estimated P 6
Riobello et al. 2018 [70] PD-L1 Spain Caucasian SSCC 53 37/16 ≥ 5% IHC I-IV OS DFS DSS reported P 5
Roper et al. 2017 [71] PD-L1 Australia Caucasian HNSCC 74 64/10 > 5% IHC NA DFS reported P 6
Satgunaseelan et al. 2016 [72] PD-L1 Australia Caucasian OSCC 217 130/87 Score ≥ 1 IHC NA DSS estimated P 6
Schneider et al. 2018 [73] PD-1 PD-L1 Austria Caucasian HNSCC 129 97/28 > 5% IHC I-IV OS DFS reported P 7
Seppälä et al. 2016 [74] IDO Finland Caucasian OSCC 58 29/29 > 0 IHC I-III OS reported P 6
Solomon et al. 2018 [75] PD-L1 Australia Caucasian OSCC 190 157/33 ≥ 5% IHC I-IV OS reported P 7
Steuer et al. 2018 [76] PD-1 USA Caucasian OPSCC 97 81/16 Score > 1 IHC I-IV OS reported P 7
Strati et al. 2017 [77] PD-L1 Greece Caucasian HNSCC 113 75/19 NA qRT-PCR I-IV OS PFS reported P 5
Straub et al. 2016 [78] PD-L1 Germany Caucasian OSCC 80 54/26 > 5% IHC FISH I-IV OS DFS estimated P 7
Tang et al. 2017 [79] PD-1 China Asian NPC 96 NA NA IHC NA OS estimated P 6
Ukpo et al. 2013 [80] PD-L1 USA Caucasian OPSCC 181 162/19 > 5% IHC I-IV OS DFS DSS reported P 7
Vassilakopoulou et al. 2016 [81] PD-L1 Greece Caucasian LSCC 260 249/11 > 59th percentile of AQUA score IHC I-IV OS DFS reported P 7
Ye et al. 2013 [82] IDO China Asian LSCC 187 179/8 NA IHC I-IV OS DFS reported P 6
Zhang et al. 2015 [83] PD-1 PD-L1 China Asian NPC 139 113/26 H-score PD-1 > 0 PD-L1 > 35 IHC I-IV DFS estimated P 7
Zheng et al. 2017 [84] PD-L1 China Asian NPC 85 63/22 Score > 2 IHC I-IV OS estimated P 6
Zhu et al. 2017 [38] PD-L1 China Asian NPC 209 150/59 ≥ 5% IHC I-IV OS DFS reported P 7
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PD-L1: programmed death ligand 1; PD-1: programmed death 1; IDO: indoleamine 2, 3-dioxygenase; M/F: male/female; NA: not available; OSCC: oral squamous cell carcinoma; HNSCC: head and neck squamous cell carcinoma; NPC: nasopharyngeal carcinoma; OPSCC: oropharyngeal squamous cell carcinoma; HPSCC: hypopharyngeal squamous cell carcinoma; SSCC: sinonasal squamous cell carcinoma; LSCC: laryngeal squamous cell carcinoma; cut-off value: the value that can be diagnosed as positive/high expression of an immune checkpoint molecule; AQUA: automated quantitative analysis; IHC: immunohistochemistry; IF: immunofluorescence; qRT-PCR: quantitative reverse transcription polymerase chain reaction; FACS: fluorescence-activated cell sorting; FISH: fluorescence in situ hybridization; OS: overall survival; DFS: disease-free survival; DMFS: distant metastases-free survival; DSS: disease-specific survival; PFS: progression-free survival; P: prospective; NOS: Newcastle–Ottawa Quality Assessment Scale.

Figure 1. Flow diagram of studies identified, included and excluded.

Figure 1

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Methodological quality of the included studies

The quality of the included studies was generally high. Most of the studies mentioned the length of the follow-up period, and the majority provided adequate follow-up data for more than five years. Nevertheless, almost none of the prospective studies had an exposed cohort that sufficiently represented the general characteristics of the population in the community, as this factor was not considered in the study design. None of the studies were designed with adequate comparability of cohorts, due to their failure to match exposed and non-exposed individuals and/or adjust for confounders. Methods for handling missing data and intention-to-treat analysis were not adequately described in the majority of the studies.

Immune checkpoint molecule expression and prognosis of HNC patients

Forty-three studies with 6225 patients reported the relationship between OS and at least one of the three immune checkpoint molecules in HNC. The expression of these molecules was detected mainly at the protein level, except for three studies that evaluated PD-L1 mRNA levels. Overexpression was defined based on cut-off criteria that differed among the studies (as presented in Table 1). When the data for all three immune checkpoint molecules were pooled, there was no significant relationship between the overexpression of these molecules and OS (hazard ratio [HR] = 0.964; 95% confidence interval [CI]: 0.791-1.175, P = 0.714; Table 2), and there was obvious overall heterogeneity (I2 = 74.8%, Ph < 0.001; Figure 2). Similar results were obtained for DFS, PFS, DSS and DMFS.

Table 2. Results of the meta-analysis on the prognostic effects of immune checkpoint molecules in HNC patients.

Variable Study no. Sample size HR (95% CI) P value Heterogeneity
I2 P value
OS Overall 43 6225 0.964 (0.791-1.175) 0.714 74.8% <0.001
Immune checkpoint molecules
PD-L1 32 4854 0.874 (0.711-1.073) 0.197 72.8% <0.001
PD-1 7 967 0.926 (0.424-2.025) 0.848 76.7% <0.001
IDO 4 404 2.197 (1.199-4.023) 0.011 59.8% 0.059
Ethnicity
Asian 19 2938 0.923 (0.651-1.307) 0.650 77.1% <0.001
Caucasian 24 3287 0.995 (0.779-1.270) 0.965 73.8% <0.001
Tumor location
OSCC 13 1477 0.879 (0.586-1.317) 0.532 85.0% <0.001
NPC 10 1008 0.862 (0.618-1.203) 0.383 33.7% 0.139
OPSCC 4 592 0.878 (0.532-1.450) 0.611 47.1% 0.129
HPSCC 1 83 1.300 (0.700-2.415) 0.407 - -
SSCC 1 53 1.355 (0.739-2.485) 0.326 - -
LSCC 2 447 1.517 (0.252-9.126) 0.649 91.4% 0.001
Sample size
Large 14 3721 1.044 (0.803-1.356) 0.748 74.0% <0.001
Small 29 2504 0.915 (0.687-1.220) 0.546 74.3% <0.001
DFS Overall 19 2901 1.097 (0.733-1.642) 0.652 92.5% <0.001
Inhibitory immune checkpoint molecules
PD-L1 13 2010 0.874 (0.523-1.459) 0.606 94.1% <0.001
IDO 2 258 1.725 (0.611-4.869) 0.303 59.5% 0.116
PD-1 4 633 1.931 (0.716-5.211) 0.194 87.5% <0.001
Ethnicity
Asian 8 1252 1.131 (0.506-2.533) 0.764 93.6% <0.001
Caucasian 11 1649 1.060 (0.760-1.479) 0.731 73.9% <0.001
Tumor location
OSCC 3 247 0.609 (0.208-1.788) 0.367 70.8% 0.033
NPC 6 666 1.339 (0.581-3.085) 0.494 92.5% <0.001
SSCC 1 53 1.834 (0.955-3.522) 0.068 - -
OPSCC 1 181 1.090 (0.783-1.518) 0.610 - -
LSCC 2 447 1.282 (0.242-6.783) 0.770 85.9% 0.008
Sample size
Large 6 1756 0.844 (0.595-1.198) 0.343 75.5% <0.001
Small 13 1145 1.225 (0.764-1.963) 0.399 88.9% <0.001
PFS Overall 6 545 0.996 (0.585-1.685) 0.989 68.5% 0.007
Inhibitory immune checkpoint molecules
PD-L1 6 545 0.891 (0.565-1.404) 0.989 68.5% 0.007
Ethnicity
Asian 3 233 0.846 (0.492-1.455) 0.744 48.3% 0.144
Caucasian 3 312 1.218 (0.372-3.993) 0.546 82.7% 0.003
Tumor location
NPC 2 227 0.762 (0.506-1.149) 0.195 0.0% 0.935
OSCC 1 84 0.576 (0.308-1.076) 0.084 - -
HPSCC 1 83 1.350 (0.740-2.463) 0.328 - -
Sample size
Large 1 161 0.770 (0.480-1.235) 0.279 - -
Small 5 384 1.067 (0.536-2.125) 0.853 73.0% 0.005
DSS Overall 5 699 0.779 (0.330-1.839) 0.569 84.7% <0.001
DMFS Overall 3 361 0.599 (0.346-1.035) 0.066 0.0% 0.604
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Figure 2. Overall forest plot of stratified analysis based on the type of molecule for the association of immune checkpoint molecules with OS.

Figure 2

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Subgroup analyses

Subgroup analyses stratified according to the immune checkpoint molecule, patient ethnicity, tumor location and sample size were performed to detect potential sources of heterogeneity. In the stratification based on the immune checkpoint molecule (Figure 2), poorer OS was consistently found in patients with higher levels of IDO (Table 2), correlating with a poorer prognosis (HR = 2.197, 95% CI: 1.199-4.023, P = 0.011, Figure 2). However, no obvious trend in DFS was found according to IDO expression (Table 2).

The same hierarchical strategy was used to evaluate the studies of PD-L1 (Table 3). Among the immune checkpoint molecules, PD-L1 was the focus of the largest percentage of studies, as 32 studies with 4854 patients reported the relationship between PD-L1 expression and OS (Figure 3). There was a possible trend for a better prognosis in patients overexpressing PD-L1 (HR = 0.874; 95% CI: 0.711-1.073, P = 0.197).

Table 3. Results of the meta-analysis on the prognostic effects of PD-L1 in HNC patients.

Variable Study no. Sample size HR (95% CI) P value Heterogeneity
I2 P value
OS Overall 32 4854 0.874 (0.711-1.073) 0.197 72.8% <0.001
Ethnicity
Asian 14 2074 0.792 (0.537-1.168) 0.240 78% <0.001
Caucasian 18 2780 0.91 (0.716-1.158) 0.444 68.2% <0.001
Tumor location
OSCC 10 1198 0.726 (0.470-1.121) 0.148 84.7% <0.001
NPC 7 795 0.692 (0.523-0.915) 0.01 0.0% 0.855
OPSCC 3 495 0.975 (0.771-1.234) 0.835 0.0% 0.403
HPSCC 1 83 1.300 (0.700-2.415) 0.407 - -
SSCC 1 53 1.355 (0.739-2.485) 0.326 - -
LSCC 1 260 0.635 (0.393-1.025) 0.063 - -
Sample size
Large 12 3132 1.022 (0.790-1.321) 0.87 71.4% <0.001
Small 20 1722 0.77 (0.575-1.031) 0.08 66.6% <0.001
DFS Overall 13 2011 0.874 (0.523-1.465) 0.607 93.9% <0.001
Ethnicity
Asian 5 617 0.824 (0.290-2.338) 0.716 94.2% <0.001
Caucasian 8 1394 0.883 (0.638-1.221) 0.451 62.4% 0.009
Tumor location
OSCC 3 247 0.610 (0.208-1.793) 0.369 70.5% 0.034
NPC 4 549 1.042 (0.349-3.111) 0.941 94.9% <0.001
SSCC 1 53 1.834 (0.955-3.522) 0.068 - -
OPSCC 1 181 1.090 (0.783-1.518) 0.610 - -
LSCC 1 260 0.591 (0.350-0.997) 0.048 - -
Sample size
Large 4 1167 0.829 (0.597-1.151) 0.263 57.5% 0.07
Small 9 844 0.900 (0.454-1.785) 0.762 91.7% <0.001
PFS Overall 7 630 0.996 (0.632-1.569) 0.986 62.1% 0.015
Ethnicity
Asian 4 318 0.879 (0.585-1.321) 0.534 24.2% 0.266
Caucasian 3 312 1.219 (0.372-3.997) 0.744 82.6% 0.003
Tumor location
OSCC 2 169 0.706 (0.416-1.197) 0.196 7.8% 0.298
HPSCC 1 83 1.350 (0.737-2.473) 0.331 - -
NPC 2 227 0.762 (0.503-1.154) 0.200 0.0% 0.935
Sample size
Large 1 161 0.770 (0.476-1.246) 0.287 - <0.001
Small 6 469 1.058 (0.600-1.876) 0.845 66.2% 0.011
DSS Overall 5 699 0.779 (0.330-1.839) 0.569 84.7% <0.001
DMFS Overall 3 361 0.599 (0.346-1.035) 0.066 0.0% 0.604
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Figure 3. Overall forest plot of stratified analysis based on the tumor location for the association between PD-L1 and OS.

Figure 3

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In nasopharyngeal carcinoma (NPC) patients, the OS was better for those expressing higher levels of PD-L1 (HR = 0.692, 95% CI: 0.523-0.915, P = 0.010). However, no obvious trend in DFS, PFS, DSS or DMFS was found according to PD-L1 expression. In laryngeal squamous cell carcinoma patients, higher PD-L1 expression was associated with better DFS (HR = 0.591, 95% CI: 0.350-0.997, P = 0.048).

Sensitivity analysis and publication bias

A sensitivity analysis of the association between the expression of PD-L1 and the prognosis of HNC patients was performed for high-quality studies (NOS score ≥ 7, Table 4). The overall HRs and 95% CIs followed the same trends as those in the previous analysis. Higher levels of PD-L1 exhibited a trend of correlation with better OS (HR = 0.754, 95% CI: 0.568-1.002, P = 0.051, Figure 4A) and were associated with better PFS (HR = 0.618, 95% CI: 0.388-0.985, P = 0.043, Figure 4B) in the high-quality studies. As in the previous analysis, the OS of NPC patients was better in the high-PD-L1 group (HR = 0.649, 95% CI: 0.458-0.920, P = 0.015, Figure 4A). The heterogeneity among the studies decreased slightly for OS, but it remained statistically significant (I2 = 76.6%, Ph < 0.001; Table 4). In addition, subgroup analyses revealed that higher PD-L1 levels were associated with better OS in Caucasian patients (HR = 0.742, 95% CI: 0.578-0.954, P = 0.020) and in studies with small sample sizes (HR = 0.582, 95% CI: 0.426-0.796, P < 0.001, Table 4).

Table 4. Sensitivity analysis results for high-quality studies on the prognostic effects of PD-L1 in HNC patients.

Variable Study no. Sample size HR (95% CI) P value Heterogeneity
I2 P value
OS Overall 17 2581 0.754 (0.568-1.002) 0.051 76.6% <0.001
Ethnicity
Asian 7 1255 0.720 (0.385-1.348) 0.305 88.1% <0.001
Caucasian 10 1326 0.742 (0.578-0.954) 0.020 46.1% 0.054
Tumor location
OSCC 5 531 0.653 (0.292-1.462) 0.300 90.7% <0.001
NPC 3 389 0.649 (0.458-0.920) 0.015 0.0% 0.744
OPSCC 3 495 0.975 (0.771-1.234) 0.835 0.0% 0.403
LSCC 1 260 0.635 (0.393-1.025) 0.063 - -
Sample size
Large 7 1715 0.984 (0.659-1.468) 0.936 79.5% <0.001
Small 10 866 0.582 (0.426-0.796) 0.001 50.3% 0.034
DFS Overall 7 1066 0.928 (0.618-1.392) 0.717 69.4% 0.003
Ethnicity
Asian 3 416 0.809 (0.241-2.720) 0.732 85.8% 0.001
Caucasian 4 650 0.938 (0.663-1.328) 0.719 43.6% 0.150
Tumor location
OSCC 2 148 0.699 (0.114-4.263) 0.697 78.4% 0.032
NPC 2 348 1.215 (0.288-5.133) 0.791 91.0% 0.001
OPSCC 1 181 1.090 (0.783-1.518) 0.610 - -
LSCC 1 260 0.591 (0.351-0.996) 0.048 - -
Sample size
Large 3 650 0.753 (0.485-1.171) 0.208 66.8% 0.049
Small 4 416 1.146 (0.536-2.450) 0.725 71.0% 0.016
PFS Overall 3 188 0.618 (0.388-0.985) 0.043 0.0% 0.867
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Figure 4.

Figure 4

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Overall forest plots of sensitivity analysis. (A) Stratified analysis based on the tumor location for the association between PD-L1 and OS. (B) Overall forest plots of sensitivity analysis for the association between PD-L1 and PFS.

Funnel plots of OS were created for all the studies (Figure 5A), for the studies on PD-L1 (Figure 5B) and for the high-quality studies on PD-L1 (Figure 5C). For all three plots, the studies were distributed uniformly around the axis, manifesting no obvious publication bias (P = 0.509, 0.876 and 0.868 for all the studies, the studies on PD-L1 and the high-quality studies on PD-L1, respectively).

Figure 5.

Figure 5

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Begg’s funnel plots of publication bias on the relationships between immune checkpoint molecules and OS in all studies (A), PD-L1-associated studies (B) and high-quality studies on PD-L1 (C).

DISCUSSION

As immune checkpoint molecules could be involved in the immune surveillance of tumor development and progression and the clearance of tumors [11], anti-immune-checkpoint drugs such as pembrolizumab [3,19], nivolumab [26,27] and ipilimumab [28] have been approved to treat melanoma, non-small cell lung cancer, renal cell carcinoma, prostate cancer and HNC. Recent studies have examined how immune checkpoint molecules, especially PD-L1, influence the prognosis of cancer patients, and a large number of updated reports have been published in the past two years. However, no consensus has been reached on the effects of immune checkpoint molecules on the prognosis of HNC.

This meta-analysis on the prognostic value of immune checkpoint molecules included 52 studies with a total of 7127 patients. The expression of immune checkpoint molecules was found to be a controversial prognostic factor for the OS, DFS, PFS, DSS and DMFS of HNC patients. Although the current view is that immune checkpoint molecules may be important predictors of a poor prognosis in HNC [17–19,22,29–31], our subgroup analysis stratified according to the immune checkpoint molecule revealed that different molecules had different associations with the patient prognosis. Thus, our results require careful attention.

Higher IDO expression was associated with a poorer prognosis for HNC patients in our study. Similarly, high IDO expression has been reported to correlate with a poor prognosis in patients with melanoma, breast cancer and colon cancer [32–34]. However, in our study, higher expression of PD-L1 tended to be associated with better OS. Kogashiwa et al. [35] found that higher expression of PD-L1 was associated with a higher number of CD8+ tumor-infiltrating lymphocytes, leading to better OS for HNC patients. Balermapas et al. and Hanna et al. [36,37] also reported higher levels of tumor-infiltrating lymphocytes in HNC patients expressing higher levels of PD-L1, which could explain the improved OS of these patients.

As PD-L1 attracted the most attention of the included immune checkpoint molecules, and a large number of updated studies reported the relationship between high levels of PD-L1 and the prognosis of HNC in 2017 and 2018, we considered it important to conduct a further meta-analysis solely on this molecule. We found that higher PD-L1 expression was associated with better OS in NPC patients, although for HNC overall there was only a positive trend, rather than a concrete link (Figure 3). A sensitivity analysis revealed the same trends in OS. In addition, higher PD-L1 expression was found to correlate with better PFS. The results of the sensitivity analysis may be more dependable than the former results, as all the included studies were of high quality. Furthermore, the same relationship between PD-L1 expression and OS was confirmed in Caucasian subjects, NPC patients and studies with small sample sizes.

Tumors can develop adaptive immune resistance, which is one of the two mechanisms regulating tumor PD-L1 expression (the second being intrinsic immune resistance) [38]. While the intrinsic mechanism leads to PD-L1 expression after oncogenic mutation [39], the adaptive mechanism causes tumor cells to express PD-L1 after they have been stimulated by interferon gamma secreted by CD8+ T cells [40,41]. Therefore, tumor-membranous PD-L1 levels could partly reflect the amount of tumor-infiltrating lymphocytes, especially cytotoxic T cells, accounting to some extent for the better survival of patients with higher PD-L1 levels.

There are several limitations to this meta-analysis. Firstly, the overall heterogeneity was high, so random effects models were required for the analysis, and there was less sensitivity to detect significant differences. Secondly, all the included studies were prospective, and the majority of studies did not have adequate random sequences or comparable cohorts, increasing the risk of bias. Thus, the quality of the included studies was not perfect. Lastly, the study populations were all of Asian or Caucasian ethnicity, which may have caused a population selection bias.

Our meta-analysis indicated that different immune checkpoint molecules correlated with different prognoses in HNC patients: higher IDO expression predicted a poorer prognosis, while higher PD-L1 expression was associated with a better prognosis. Furthermore, our study revealed that higher expression of PD-L1 was associated with significantly better OS in Caucasian subjects, NPC patients and studies with small sample sizes. In summary, our study suggested that the immune checkpoint molecules IDO and PD-L1 have potential prognostic value and applicability to immune therapy for HNC.

METHODS

Literature-search strategy

This literature search was performed on August 10, 2018 without any restrictions in region, publication type, journal or language. The databases of PubMed, Embase and the Cochrane Library were thoroughly searched with the following strategy: ((((((((((((((((((((((((((head and neck cancer [Title/Abstract]) OR head and neck squamous cell carcinoma[Title/Abstract]) OR head and neck neoplasm$[Title/Abstract]) OR HNSCC[Title/Abstract]) OR SCCHN[Title/Abstract]) OR HNC[Title /Abstract]) OR mouth neoplasms[Title /Abstract]) OR cancers of mouth[Title/Abstract]) OR oral[Title/Abstract]) OR laryn*[Title/Abstract]) OR pharyn*[Title/Abstract]) OR tongue[Title/Abstract]) OR oropharyn*[Title/Abstract]) OR nasopharyn*[Title/Abstract]) OR hypopharyn*[Title/Abstract]) OR trachea[Title/Abstract]) OR laryngopharyn*[Title /Abstract]) OR cervical tracheal[Title/Abstract]) OR cervical esophagus[Title/Abstract]) OR lip[Title /Abstract])) OR sinonasal[Title/Abstract]) OR head and neck cutaneous squamous cell carcinoma[Title/Abstract]) OR squamous cell carcinoma of the oral cavity[Title/Abstract]) OR salivary gland carcinoma[Title/Abstract]) OR SGC[Title/Abstract]) AND (((((((((((((((((((((((((b7-h3[Title/Abstract]) OR cd276[Title/Abstract]) OR b7-h4[Title/Abstract]) OR vtcn1[Title/Abstract]) OR btla[Title/Abstract]) OR b and t lymphocyte attenuator) OR cd272[Title/Abstract]) OR ctla-4[Title/Abstract]) OR cytotoxic t-lymphocyte-associated protein 4[Title/Abstract]) OR cd152[Title /Abstract]) OR ido[Title/Abstract]) OR indoleamine 2,3-dioxygenase[Title/Abstract]) OR kir[Title/Abstract]) OR killer-cell immunoglobulin-like receptor[Title/Abstract]) OR lag3[Title/Abstract]) OR lymphocyte activation gene-3[Title/Abstract]) OR pd-1[Title/Abstract]) OR programmed death 1 receptor[Title/Abstract]) OR pd-l1[Title/Abstract]) OR programmed death ligand 1[Title/Abstract]) OR pd-l2[Title/Abstract]) OR tim-3[Title/Abstract]) OR t-cell immunoglobulin domain and mucin domain 3[Title/Abstract]) OR vista[Title/Abstract]) OR v-domain ig suppressor of t cell activation[Title/Abstract]) OR b7-h1[Title/Abstract]). Two reviewers (Y.Q.J. and B.Y.) inspected all candidate articles independently. Discrepancies were resolved by discussion with the senior authors (Z.W. and B.C.).

Inclusion and exclusion criteria

The available prospective comparative studies (cohort studies) were included in this study based on their conformance to the following inclusion criteria: 1) the association of immune checkpoint marker expression with OS/DFS/PFS/DSS/DMFS in HNC was reported; 2) the diagnosis of HNC was made based on pathological examination; 3) HRs and 95% CIs were provided or could be estimated from the text; 4) only the more recent or complete article was selected when multiple reports described the same population, to avoid the duplicate inclusion of data; and 5) articles were published as original research.

The exclusion criteria were: 1) reviews, meeting abstracts, letters; 2) animal model studies; 3) sample size < 30 patients; 4) insufficient data to estimate the HR and 95% CI; 5) the main type of tumor was not SCC; 6) the number of studies on a single molecule was less than three; and 7) the study design was not prospective.

Data extraction and quality assessment

Two reviewers (Y.Q.J. and B.Y.) extracted the following information independently from the included studies: author, year of publication, study country or region, sample ethnicity, tumor location, follow-up period, sample size, gender, cut-off values of immune checkpoint molecules, detection method, TNM stage, and survival data such as OS, DFS, PFS, DSS and DMFS. The HR and 95% CI were either reported or calculated from the P value or Kaplan-Meier survival curve [42,43]. Disagreements were resolved by a senior reviewer (Z.W.).

Two reviewers (L.L.W. and W.X.M.) independently assessed the quality of the included studies by the NOS. A score of 0–9 was given to each study, and studies with NOS scores ≥ 7 were defined as high-quality. Consensus was reached by discussion with senior reviewers (B.C. and Z.W.) when there were inconsistent results. Importantly, the procedure of assessing the quality of the studies was blinded to the reviewers who extracted the data (Y.Q.J. and B.Y.).

Statistical analysis

This meta-analysis was performed in accordance with recommendations from the Cochrane Collaboration and the Quality of Reporting of Meta-analyses guidelines [44,45]. The HR was used as a summary statistic for censored outcomes (OS, DFS, PFS, DSS and DMFS). HRs > 1 represented a poor prognosis in HNC.

Heterogeneity among the primary studies was evaluated by Cochrane’s Q statistic and the I2 statistic. A P value < 0.10 in Cochrane’s Q test or an I2 value > 50% indicates substantial heterogeneity among studies, so a random effects model was used to calculate the pooled HR and 95% CI in such cases. Otherwise, a fixed effects model was applied.

We used the mean sample size as the boundary between studies with large and small sample sizes. Subgroup analyses were carried out according to the immune checkpoint molecule, ethnicity, sample size and tumor location. Sensitivity analysis was applied to high-quality studies (NOS ≥ 7). Begg’s funnel plots were used to assess publication bias. All statistical analyses were conducted with STATA 12.0 statistical software (Stata Corporation, College Station, TX, USA). A two-tailed P value < 0.05 was considered statistically significant.

SUPPLEMENTARY MATERIAL

Supplementary Table 1
aging-11-101756-s001.docx (23.3KB, docx)

Abbreviations

PD-L1

programmed death ligand 1

PD-1

programmed death 1

LAG-3

lymphocyte activation gene 3

CTLA-4

cytotoxic T-lymphocyte-associated protein 4

PD-L2

programmed death ligand 2

IDO

indoleamine 2, 3-dioxygenase

VISTA

V-domain Ig suppressor of T cell activation

HR

hazard ratio

M/F

male/female

NA

not available

HNC

head and neck cancer

HNSCC

head and neck squamous cell carcinoma

OSCC

oral squamous cell carcinoma

NPC

nasopharyngeal carcinoma

OPSCC

oropharyngeal squamous cell carcinoma

LSCC

laryngeal squamous cell carcinoma

cut-off value

the value that can be diagnosed as positive/high expression of an immune checkpoint molecule

AQUA

automated quantitative analysis

IHC

immunohistochemistry

IF

immunofluorescence

qRT-PCR

quantitative reverse transcription polymerase chain reaction

FACS

fluorescence-activated cell sorting

FISH

fluorescence in situ hybridization

OS

overall survival

PFS

progression-free survival

DFS

disease-free survival

DSS

disease-specific survival

DMFS

distant metastases-free survival

P

prospective

NOS

Newcastle–Ottawa Quality Assessment Scale

CI

confidence interval

Footnotes

AUTHOR CONTRIBUTIONS: Y.Q.J. and B.Y. conceived and designed the research, analyzed the data and wrote the paper. Z.W. and B.C. reviewed drafts of the paper and participated in its design and coordination. L.L.W. and W.X.M. evaluated the quality of the literature and prepared the figures and tables. All the authors read and approved the final manuscript.

CONFLICTS OF INTEREST: The authors declare no conflict of interest.

FUNDING: This study was supported by the National Natural Science Foundation of China (No. 81630025, 81772896, 81472524, 81630025) and the Natural Science Foundation of Guangdong Province (No. 2017A030311033).

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Supplementary Table 1
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